NDLI: Data Unpredictability in Software Defect-Fixing Effort Prediction

Content Provider	IEEE Xplore Digital Library
Author	Zhimin He Fengdi Shu Ye Yang Wen Zhang Qing Wang
Copyright Year	2010
Abstract	The prediction of software defect-fixing effort is important for strategic resource allocation and software quality management. Machine learning techniques have become very popular in addressing this problem and many related prediction models have been proposed. However, almost every model today faces a challenging issue of demonstrating satisfactory prediction accuracy and meaningful prediction results. In this paper, we investigate what makes high-precision prediction of defect-fixing effort so hard from the perspective of the characteristics of defect dataset. We develop a method using a metric to quantitatively analyze the unpredictability of a defect dataset and carry out case studies on two defect datasets. The results show that data unpredictability is a key factor for unsatisfactory prediction accuracy and our approach can explain why high-precision prediction for some defect datasets is hard to achieve inherently. We also provide some suggestions on how to collect highly predictable defect data.
Starting Page	220
Ending Page	226
File Size	387865
Page Count	7
File Format	PDF
ISBN	9781424480784
ISSN	15506002
e-ISBN	9780769541310
DOI	10.1109/QSIC.2010.40
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-07-14
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Predictive models Accuracy Software Support vector machines Machine learning Data models Prediction algorithms machine learning data unpredictability MAE software defect-fixing effort prediction
Content Type	Text
Resource Type	Article

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